scholarly journals An Evolutionary Method for Combining Different Feature Selection Criteria in Microarray Data Classification

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Nicoletta Dessì ◽  
Barbara Pes
Keyword(s):  
Microarray Data ◽  
Selection Criteria ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Research Area ◽  
Svm Classifier ◽  
Ranking Methods ◽  
Wrapper Approach ◽  
Relevant Gene

The classification of cancers from gene expression profiles is a challenging research area in bioinformatics since the high dimensionality of microarray data results in irrelevant and redundant information that affects the performance of classification. This paper proposes using an evolutionary algorithm to select relevant gene subsets in order to further use them for the classification task. This is achieved by combining valuable results from different feature ranking methods into feature pools whose dimensionality is reduced by a wrapper approach involving a genetic algorithm and SVM classifier. Specifically, the GA explores the space defined by each feature pool looking for solutions that balance the size of the feature subsets and their classification accuracy. Experiments demonstrate that the proposed method provide good results in comparison to different state of art methods for the classification of microarray data.

Dorsal Root Ganglion Neuron Types and Their Functional Specialization

2018 ◽  
pp. 127-155 ◽  
Author(s):  
Edward C. Emery ◽  
Patrik Ernfors
Keyword(s):  
Chronic Pain ◽  
Dorsal Root Ganglion ◽  
Sensory Neurons ◽  
Dorsal Root ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Drg Neurons ◽  
Low Threshold

Primary sensory neurons of the dorsal root ganglion (DRG) respond and relay sensations that are felt, such as those for touch, pain, temperature, itch, and more. The ability to discriminate between the various types of stimuli is reflected by the existence of specialized DRG neurons tuned to respond to specific stimuli. Because of this, a comprehensive classification of DRG neurons is critical for determining exactly how somatosensation works and for providing insights into cell types involved during chronic pain. This article reviews the recent advances in unbiased classification of molecular types of DRG neurons in the perspective of known functions as well as predicted functions based on gene expression profiles. The data show that sensory neurons are organized in a basal structure of three cold-sensitive neuron types, five mechano-heat sensitive nociceptor types, four A-Low threshold mechanoreceptor types, five itch-mechano-heat–sensitive nociceptor types and a single C–low-threshold mechanoreceptor type with a strong relation between molecular neuron types and functional types. As a general feature, each neuron type displays a unique and predicable response profile; at the same time, most neuron types convey multiple modalities and intensities. Therefore, sensation is likely determined by the summation of ensembles of active primary afferent types. The new classification scheme will be instructive in determining the exact cellular and molecular mechanisms underlying somatosensation, facilitating the development of rational strategies to identify causes for chronic pain.

scholarly journals A Supervised Network Analysis on Gene Expression Profiles of Breast Tumors Predicts a 41-Gene Prognostic Signature of the Transcription FactorMYBacross Molecular Subtypes

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Li-Yu D. Liu ◽  
Li-Yun Chang ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
King-Jen Chang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Essential Gene ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prognostic Signature ◽  
Prognostic Features ◽  
Bioinformatic Tools ◽  
Prognostic Feature ◽  
Relevant Gene

Background. MYBis predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features ofMYBvia a supervised network analysis.Methods. Both coefficient of intrinsic dependence (CID) and Galton Pierson’s correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included.Results. ARNT2is predicted to be the essential gene partner ofMYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I–IV. Only the probes in feature type II are the potential prognostic feature ofMYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members ofMYBare elevated to promote poor prognosis when both levels ofMYBandARNT2decline. BothMYBL1andMYBL2may partially decrease the tumor suppressive activities that are predicted to be up-regulated byMYBandARNT2.Conclusions. The major prognostic feature ofMYBis predicted to be determined by theMYBsubnetwork (41 probes) that is relevant across subtypes.

A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

2004 ◽  
Vol 3 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Markus Ruschhaupt ◽  
Wolfgang Huber ◽  
Annemarie Poustka ◽  
Ulrich Mansmann
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Statistical Processing ◽  
Primary Data ◽  
High Dimensional ◽  
Microarray Gene Expression ◽  
Data Set ◽  
Dimensional Classification ◽  
Biological Interpretation

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

scholarly journals The derivation of diagnostic markers of chronic myeloid leukemia progression from microarray data

Blood ◽  
2009 ◽  
Vol 114 (15) ◽  
pp. 3292-3298 ◽  
Author(s):  
Vivian G. Oehler ◽  
Ka Yee Yeung ◽  
Yongjae E. Choi ◽  
Roger E. Bumgarner ◽  
Adrian E. Raftery ◽  
...  
Keyword(s):  
Chronic Myeloid Leukemia ◽  
Microarray Data ◽  
Myeloid Leukemia ◽  
Bayesian Model Averaging ◽  
Expression Profiles ◽  
Blast Crisis ◽  
Probabilistic Method ◽  
Gene Expression Profiles ◽  
Chronic Phase ◽  
Microarray Dataset

Abstract Currently, limited molecular markers exist that can determine where in the spectrum of chronic myeloid leukemia (CML) progression an individual patient falls at diagnosis. Gene expression profiles can predict disease and prognosis, but most widely used microarray analytical methods yield lengthy gene candidate lists that are difficult to apply clinically. Consequently, we applied a probabilistic method called Bayesian model averaging (BMA) to a large CML microarray dataset. BMA, a supervised method, considers multiple genes simultaneously and identifies small gene sets. BMA identified 6 genes (NOB1, DDX47, IGSF2, LTB4R, SCARB1, and SLC25A3) that discriminated chronic phase (CP) from blast crisis (BC) CML. In CML, phase labels divide disease progression into discrete states. BMA, however, produces posterior probabilities between 0 and 1 and predicts patients in “intermediate” stages. In validation studies of 88 patients, the 6-gene signature discriminated early CP from late CP, accelerated phase, and BC. This distinction between early and late CP is not possible with current classifications, which are based on known duration of disease. BMA is a powerful tool for developing diagnostic tests from microarray data. Because therapeutic outcomes are so closely tied to disease phase, these probabilities can be used to determine a risk-based treatment strategy at diagnosis.

scholarly journals Organ-Specific Molecular Classification of Primary Lung, Colon, and Ovarian Adenocarcinomas Using Gene Expression Profiles

2001 ◽  
Vol 159 (4) ◽  
pp. 1231-1238 ◽  
Author(s):  
Thomas J. Giordano ◽  
Kerby A. Shedden ◽  
Donald R. Schwartz ◽  
Rork Kuick ◽  
Jeremy M.G. Taylor ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Classification ◽  
Organ Specific ◽  
Ovarian Adenocarcinomas
Sign in / Sign up

Export Citation Format

Share Document